Post-processing Temperature Research Articles

Implementation of Snowpack Treatment in the CPC Water Balance Model and Its Impact on Drought Assessment

AbstractDroughts are a worldwide concern, thus assessment efforts are conducted by many centers around the world, mainly through simple drought indices which usually neglect important hydrometeorological processes or require variables available only from complex Land Surface Models (LSMs). The U.S. Climate Prediction Center (CPC) uses the Leaky Bucket (LB) water-balance model to post-process temperature and precipitation, providing soil moisture (SM) anomalies to assess drought conditions. However, despite its crucial role in the water cycle, snowpack has been neglected by LB and most drought indices.Taking advantage of the high-quality snow water equivalent (SWE) data from the University of Arizona (UA), a single-layer snow scheme, forced by daily temperature and precipitation only, is developed for LB implementation and tested with two independent forcing datasets. Compared against the UA and SNOTEL SWE data over CONUS, LB outperforms a sophisticated LSM (Noah/NLDAS-2), with the median LB vs SNOTEL correlation (RMSE) about 40% (26%) higher (lower) than that from Noah/NLDAS-2, with only slight differences due to different forcing datasets.The changes in the temporal variability of SM due to the snowpack treatment lead to improved temporal and spatial distribution of drought conditions in the LB simulations compared to the reference U.S. Drought Monitor maps, highlighting the importance of snowpack inclusion in drought assessment. The simplicity but reasonable reliability of the LB with snowpack treatment makes it suitable for drought monitoring and forecasting in both snow-covered and snow-free areas, while only requiring precipitation and temperature data (markedly less than other water-balance-based indices).

Post-processing effects on microstructure, interlaminar and thermal properties of 3D printed continuous carbon fibre composites

3D printing of continuous carbon fibre reinforced composites (CCFRC) based on fused filament fabrication (FFF) has a great potential for reproducing robust and complex geometries, generating little waste. However, 3D printed CCFRC present relatively poor interlaminar bonding and high void content in comparison to traditional composites. This work analyses the post-processing temperature effects on microstructure, interlaminar properties and thermal stability of the printed CCFRC. Treated pieces at 150 °C showed a reduction of porosity by approximately 87% and improved interlaminar strength by 145%, without modifying nominal dimensions. In addition, the increase in Tg (from 109 °C to 131 °C) is ascribed to a drying effect during the post-processing, which reduces the plasticisation of the treated parts and contributes to the general enhancement of mechanical properties of printed CCFRC coupons. The proposed post-processing moves this technology towards the development of functional composite components for high-performance structural applications.

The Effects of Post-processing in Additively Manufactured 316L Stainless Steels

This paper provides a systematic examination of microstructure and corrosion behavior of additively manufactured 316L stainless steel after post-processing by surface finishing, isothermal heat treating, or hot isostatic pressing. The effects of isothermal heat treatments from 500 °C to 1300 °C and hot isostatic press processing from 1000 °C to 1200 °C were correlated to the evolution of microstructure, pore morphology and volume fraction, microhardness measurements, and corrosion behavior to reveal the different post-processing temperature regimes and their corresponding characteristics. In particular, this study found that the AM 316L microstructures and properties are stable up to nearly 800 °C. Higher temperatures eliminate the fine solidification structure, causing a drop in microhardness and corrosion resistance. Corrosion was primarily driven by the porosity content of the exposed surface and near-surface regions, which is unaffected by isothermal or HIP post-processing, and the corrosion was not significantly affected by recrystallization, which begins around 1050 °C and is nearly complete by 1200 °C. The amount of porosity was not markedly affected by isothermal post-processing, but could be significantly reduced by HIP processing.

Making the Output of Seasonal Climate Models More Palatable to Agriculture: A Copula-Based Postprocessing Method

AbstractSeasonal climate forecasts from raw climate models at coarse grids are often biased and statistically unreliable for credible crop prediction at the farm scale. We develop a copula-based postprocessing (CPP) method to overcome this mismatch problem. The CPP forecasts are ensemble based and are generated from the predictive distribution conditioned on raw climate forecasts. CPP performs univariate postprocessing procedures at each station, lead time, and variable separately and then applies the Schaake shuffle to reorder ensemble sequence for a more realistic spatial, temporal, and cross-variable dependence structure. The use of copulas makes CPP free of strong distributional assumptions and flexible enough to describe complex dependence structures. In a case study, we apply CPP to postprocess rainfall, minimum temperature, maximum temperature, and radiation forecasts at a monthly level from the Australian Community Climate and Earth-System Simulator Seasonal model (ACCESS-S) to three representative stations in Australia. We evaluate forecast skill at lead times of 0–5 months on a cross-validation theme in the context of both univariate and multivariate forecast verification. When compared with forecasts that use climatological values as the predictor, the CPP forecast has positive skills, although the skills diminish with increasing lead times and finally become comparable at long lead times. When compared with the bias-corrected forecasts and the quantile-mapped forecasts, the CPP forecast is the overall best, with the smallest bias and greatest univariate forecast skill. As a result of the skill gain from univariate forecasts and the effect of the Schaake shuffle, CPP leads to the most skillful multivariate forecast as well. Further results investigate whether using ensemble mean or additional predictors can enhance forecast skill for CPP.

Degradation and Regeneration of n +-Doped Poly-Si Surface Passivation on p-Type and n-Type Cz-Si Under Illumination and Dark Annealing

Degradation and regeneration of recombination parameters can occur in the bulk and at the surfaces of silicon solar cells. This article focuses on the time-resolved analysis of the recombination properties of textured 1.7 Ω cm boron-doped p -type Cz-Si and 5 Ω cm phosphorus-doped n -type Cz-Si wafers, where the surfaces are passivated by n + poly-Si on interfacial oxide layers exposed to a rapid thermal annealing (RTA) step in a conventional firing furnace. We observe a thermally activated instability in the lifetime over the entire examined injection range. Our experiments show that minority carrier injection (e.g., by illumination) is not required. Degradation in the surface passivation quality of the poly-Si on oxide layer—corresponding to an increase of the saturation current density J 0 by up to a factor of five—causes the degradation of the effective lifetime. Interestingly, the surface passivation fully regenerates under prolonged annealing and finally improves even beyond the initial state. Both the extent of the lifetime degradation and the change in J 0 depend on the postprocessing treatment temperature which we varied between 80 and 400 °C. Our results indicate that two different processes are responsible for the degradation and the regeneration. Reference samples which did not receive an RTA treatment show no degradation of the surface passivation quality. The RTA treatment applied therefore triggers the degradation effect. A large improvement of the surface passivation quality under prolonged annealing (e.g., at 400 °C) is observed for all samples examined in this study.

Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy.

There are two challenges associated with the interpretability of deep learning models in medical image analysis applications that need to be addressed: confidence calibration and classification uncertainty. Confidence calibration associates the classification probability with the likelihood that it is actually correct - hence, a sample that is classified with confidence X% has a chance of X% of being correctly classified. Classification uncertainty estimates the noise present in the classification process, where such noise estimate can be used to assess the reliability of a particular classification result. Both confidence calibration and classification uncertainty are considered to be helpful in the interpretation of a classification result produced by a deep learning model, but it is unclear how much they affect classification accuracy and calibration, and how they interact. In this paper, we study the roles of confidence calibration (via post-process temperature scaling) and classification uncertainty (computed either from classification entropy or the predicted variance produced by Bayesian methods) in deep learning models. Results suggest that calibration and uncertainty improve classification interpretation and accuracy. This motivates us to propose a new Bayesian deep learning method that relies both on calibration and uncertainty to improve classification accuracy and model interpretability. Experiments are conducted on a recently proposed five-class polyp classification problem, using a data set containing 940 high-quality images of colorectal polyps, and results indicate that our proposed method holds the state-of-the-art results in terms of confidence calibration and classification accuracy.

Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression

Abstract. Non-homogeneous regression is a frequently used post-processing method for increasing the predictive skill of probabilistic ensemble weather forecasts. To adjust for seasonally varying error characteristics between ensemble forecasts and corresponding observations, different time-adaptive training schemes, including the classical sliding training window, have been developed for non-homogeneous regression. This study compares three such training approaches with the sliding-window approach for the application of post-processing near-surface air temperature forecasts across central Europe. The predictive performance is evaluated conditional on three different groups of stations located in plains, in mountain foreland, and within mountainous terrain, as well as on a specific change in the ensemble forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) used as input for the post-processing. The results show that time-adaptive training schemes using data over multiple years stabilize the temporal evolution of the coefficient estimates, yielding an increased predictive performance for all station types tested compared to the classical sliding-window approach based on the most recent days only. While this may not be surprising under fully stable model conditions, it is shown that “remembering the past” from multiple years of training data is typically also superior to the classical sliding-window approach when the ensemble prediction system is affected by certain model changes. Thus, reducing the variance of the non-homogeneous regression estimates due to increased training data appears to be more important than reducing its bias by adapting rapidly to the most current training data only.

Ad Hoc Heat Treatments for Selective Laser Melted Alsi10mg Alloy Aimed at Stress-Relieving and Enhancing Mechanical Performances

The response of an AlSi10Mg alloy produced by selective laser melting (SLM) to specifically designed heat treatments is explored. Thermal phenomena of the rapidly solidified material were studied by differential scanning calorimetry (DSC), and the results have been used to define specific post-processing thermal treatment temperatures. In detail, three heat treatments were studied: two innovative ones, able to selectively trigger (1) Mg2Si precipitation and (2) the rupture and spheroidization of the silicon network, and (3) a T5-like aging were applied for different times and their effects on mechanical properties were studied. The first and the second heat treatments were selected by combining DSC analysis and aging curves for the optimization of the temperature and duration. And these were more deeply investigated in terms of microstructural evolution and of residual stresses. The optimized thermal treatments for SLM-built AlSi10Mg are recommended as valid alternatives to the usually applied T6 or high-temperature stress release treatments.

A Hybrid Process for Printing Pure and High Conductivity Nanocrystalline Copper and Nickel on Flexible Polymeric Substrates

Printing functional devices on flexible substrates requires printing of high conductivity metallic patterns. To prevent deformation and damage of the polymeric substrate, the processing (printing) and post-processing (annealing) temperature of the metal patterns must be lower than the glass transition temperature of the substrate. Here, a hybrid process including deposition of a sacrificial blanket thin film, followed by room environment nozzle-based electrodeposition, and subsequent etching of the blanket film is demonstrated to print pure and nanocrystalline metallic (Ni and Cu) patterns on flexible substrates (PI and PET). Microscopy and spectroscopy showed that the printed metal is nanocrystalline, solid with no porosity and with low impurities. Electrical resistivity close to the bulk (~2-time) was obtained without any thermal annealing. Mechanical characterization confirmed excellent cyclic strength of the deposited metal, with limited degradation under high cyclic flexure. Several devices including radio frequency identification (RFID) tag, heater, strain gauge, and temperature sensor are demonstrated.

Precise Control over Polymer Semiconducting Films by Tuning the Thermal Behavior of the Thin-Film State's Crystalline and Morphological Structures.

The crystalline and morphological structures of polymer semiconducting films were controlled by selecting appropriate thermal properties of the polymeric chains, thereby improving polymer field-effect transistor (FET) performances. Poly(dioctyl-quaterthiophene-dioctyl-bithiazole) (PDQDB), comprising 5,5'-bithiazole and oligothiophene rings, was used as the basis for the polymer semiconductor studies. The Tg and Tm values of the thin-film state, rather than those of the bulk polymer state, were important in this study. A PDQDB film with a Tg of 101 °C in the thin-film state showed the highest maximum and average μFET values of 0.194 and 0.141 cm2 V-1 s-1, respectively, in an FET device at a post-processing temperature of 100 °C. On the other hand, relatively low average μFET values of 0.115, 0.098, and 0.079 cm2 V-1 s-1 were observed in FET devices prepared from PDQDB films with Tg values of 130, 165, and 180 °C, respectively, despite the dramatic increase in film crystallinity. With the variations in μFET, what we have noticed is that the standard deviations of the measured μFET values varied with the Tg values: 36.0% for the Tg = 165 °C film and 51.1% for the Tg = 180 °C film, indicating that the organic field-effect transistors performances were not uniform. These results were closely related to nano- and microscale nonuniformity in the PDQDB film structure in the presence of excessively activated grain structures. These variations were correlated with the crystalline and morphological structures of the PDQDB films prepared under various processing conditions.

Low-temperature formation of high-mobility a-InGaZnOx films using plasma-enhanced reactive processes

This work demonstrated the low-temperature fabrication of high-mobility a-InGaZnOx (a-IGZO) films using plasma-enhanced reactive processing. The effect of the post-processing temperature on the performance of IGZO thin-film transistors (TFTs) using these a-IGZO films as a channel layer was also investigated. IGZO TFTs incorporating a-IGZO films plasma-treated at temperatures as low as 200 °C exhibited the expected TFT transfer characteristics, with field effect mobility values as high as 40 cm2 V−1 s−1. The fabrication of IGZO TFTs that exhibited homogeneous characteristics over large substrate areas was attempted, by assessing the effects of the plasma treatment on a-IGZO films deposited at various oxygen flow rate ratios. The electrical properties of IGZO TFTs using plasma-treated films were found to be dramatically improved, irrespective of the film deposition conditions prior to the plasma treatment. These results confirm the feasibility of producing IGZO TFTs with uniform characteristics over large substrates areas.

Micro-fabricated alkali vapor cells sealed at low temperature using asymmetric Au–In transient liquid phase (TLP) bonding

Vapor cells as the key part of chip-scale atomic clocks determine their volume stability and power consumption. Micro-fabrication, especially the cavity packaging, is challenging. In this paper, micro-fabricated rubidium vapor cells are fabricated and the Au-In TLP bonding with asymmetric metal configuration is proposed and designed to package wafer-scale cell chips at a low processing temperature of 200 °C. Tests show that the designed intermetallic compounds AuIn2 and AuIn are uniformly formed in the asymmetric TLP bonding interlayer, indicating that the bonded interlayer can endure a post-processing temperature as high as 490 °C. The TLP bonded surface exhibits shear strength of 31.68 MPa, and the upper limit for the leak rate of the sealed vapor cell is 5 × 10−10 mbar · L s–1 which remains stable after undergoing a high temperature post-treatment. Meaning the proposed Au-In TLP bonding technique is applicable at low-temperature, hermetic and reliable wafer-scale packaging for micro-fabricated alkali vapor cells.

Post-processing Multiensemble Temperature and Precipitation Forecasts Through an Exchangeable Normal-Gamma Model and Its Tobit Extension

Meteorological ensembles are a collection of scenarios for future weather delivered by a meteorological center. Such ensembles form the main source of valuable information for probabilistic forecasting which aims at producing a predictive probability distribution of the quantity of interest instead of a single best guess estimate. Unfortunately, ensembles cannot generally be considered as a sample from such a predictive probability distribution without a preliminary post-processing treatment to calibrate the ensemble. Two main families of post-processing methods, either competing such as BMA or collaborative such as EMOS, can be found in the literature. This paper proposes a mixed effect model belonging to the collaborative family. The structure of the model is based on the hypothesis of invariance under the relabelling of the ensemble members. Its interesting specificities are as follows: 1) exchangeability, which contributes to parsimony, with a latent pivot variable synthesizing the essential meteorological features of the ensembles, 2) a multi-ensemble implementation, allowing to take advantage of various information so as to increase the sharpness of the forecasting procedure. Focus is cast onto Normal statistical structures, first with a direct application for temperatures, then with its Tobit extension for precipitation. Inference is performed by EM algorithms with recourse made to stochastic conditional simulations in the precipitation case. After checking its good behavior on artificial data, the proposed post-processing technique is applied to temperature and precipitation ensemble forecasts produced over five river basins managed by Hydro-Qu$\'e$bec. These ensemble forecasts were extracted from the THORPEX Interactive Grand Global Ensemble (TIGGE) database. The results indicate that post-processed ensemble are calibrated and generally sharper than the raw ensembles.

Structural and Magnetic Properties of Fe, Mn Co-doped BaTiO3 Synthesized Using Ball Milling Technique

Nano composite, monophasic nano-crystalline as well as bulk single phase materials of Fe, Mn co-doped BaTiO3 compounds are successfully synthesized through two different strategies using high energy meachnical attritor and muffle furnace. Significant changes in the structural properties such as formation of coexisting tetragonal, cubic and orthorhombic phases are brought out by varying post-processing temperature. A weak ferromagnetic signature is observed for nanocomposites and it ceased to exist with increasing post heating temperatures. Interestingly, superparamagnetic state is observed for nano-composites with blocking temperature of 263 K for a measurement field of 500 Oe. Further, this nanocomposite found to exhibit magnetic ordering well above room temperature which facilitates the possible interplay of multiple ferroic phases.

Self-Temperature-Compensated Air-Filled Substrate-Integrated Waveguide Cavities and Filters

In this paper, patent pending self-temperature-compensated air-filled substrate-integrated waveguide (AFSIW) cavities and filters are proposed and demonstrated for high-performance and low-cost components, circuits, and systems on substrate. First, a theoretical multiphysics transmission line model is developed for the synthesis of self-temperature-compensated air-filled cavities. It is determined that temperature compensation can be achieved using an appropriate substrate material and dielectric sidewall slab width. Moreover, a postprocess temperature compensation technique is introduced to counterbalance potential fabrication errors. For demonstration purposes, a self-temperature-compensated first-order AFSIW filter (based on a single cavity) is fabricated and compared to its dielectric-less temperature-uncompensated counterpart. Then, a self-temperature-compensated fourth-order AFSIW filter centered at 21 GHz with 300-MHz bandwidth is designed and fabricated. It achieves a measured unloaded $Q$ -factor of 975 at ambient temperature and a measured central frequency thermal stability as low as 2.26 ppm/°C at −40 °C and 1.67 ppm/°C at 80 °C (without using any postprocess tuning), compared to −17.4 ppm/°C at −40 °C and −18.38 ppm/°C at 80 °C for its dielectric-less temperature-uncompensated counterpart.

Rheological and curing behaviors of high heat-resistant benzoxazine resin

A new monofunctional benzoxazine resin with high char yield has been synthesized in this paper. This resin's viscosity is lower than 500 mPa s at a temerature of 55 °C, lasting for over 210 min. The experimental results on this resin's chemical rheological properties are consistent with the dual Arrhenius equation. The results of dynamic DSC analysis show that, this resin's approximate gelling temperature, curing temperature and post-processing temperature are 182.6 °C, 194.2 °C and 205.1 °C respectively. According to the Kissinger equation, this resin's activation energy of curing reaction is 78.8 kJ/mol. In addition, this resin exhibits good thermal stability: its maximum thermal decomposition rate is reached at a temperature of 479 °C, its char yield at 800 °C is 42.7 percent, and its heat-resistance index is 179.8 °C. Thus, this monocyclic benzoxazine resin is a promissing material for high performance composites.

Enhancing photocatalytic activities of titanium dioxide via well-dispersed copper nanoparticles

The modification of titanium dioxide (TiO2) using noble metal nanoparticles is considered as a promising technique to make electrode with outstanding photocatalytic performance. In this paper, self-organized anodic TiO2 nanotube arrays were decorated with well-distributed small Cu nanoparticles through a novel technique that combines magnetron sputtering and thermal dewetting. The obtained nanocomposite catalyst exhibited 4-fold increase in the photodegradation rate of methylene blue aqueous solution under solar light irradiation than anatase TiO2 prepared with same anodization conditions. The enhanced photocatalytic activity was attributed to the synergistic effect of Schottky barrier and Surface plasmon resonance. The influence of post annealing process, sputtering time and thermal dewetting temperature on photocatalytic performance was studied and the optimal preparation conditions were proposed. The results of this study may provide a new strategy to improve photocatalytic efficiency of TiO2 without using high-cost noble metals.

Predictive Uncertainty Estimation on a Precipitation and Temperature Reanalysis Ensemble for Shigar Basin, Central Karakoram

The Upper Indus Basin (UIB) and the Karakoram Range are the subject of ongoing hydro-glaciological studies to investigate possible glacier mass balance shifts due to climatic change. Because of the high altitude and remote location, the Karakoram Range is difficult to access and, therefore, remains scarcely monitored. In situ precipitation and temperature measurements are only available at valley locations. High-altitude observations exist only for very limited periods. Gridded precipitation and temperature data generated from the spatial interpolation of in situ observations are unreliable for this region because of the extreme topography. Besides satellite measurements, which offer spatial coverage, but underestimate precipitation in this area, atmospheric reanalyses remain one of the few alternatives. Here, we apply a proven approach to quantify the uncertainty associated with an ensemble of monthly precipitation and temperature reanalysis data for 1979–2009 in Shigar Basin, Central Karakoram. A Model-Conditional Processor (MCP) of uncertainty is calibrated on precipitation and temperature in situ data measured in the proximity of the study region. An ensemble of independent reanalyses is processed to determine the predictive uncertainty of monthly observations. As to be expected, the informative gain achieved by post-processing temperature reanalyses is considerable, whereas significantly less gain is achieved for precipitation post-processing. The proposed approach indicates a systematic assessment procedure for predictive uncertainty through probabilistic weighting of multiple re-forecasts, which are bias-corrected on ground observations. The approach also supports an educated reconstruction of gap-filling for missing in situ observations.

Silicon heterojunction solar cell passivation in combination with nanocrystalline silicon oxide emitters

Silicon heterojunction solar cells (SHJ) are well known for their high efficiencies, enabled by their remarkably high open‐circuit voltages (VOC). A key factor in achieving these values is a good passivation of the crystalline wafer interface. One of the restrictions during SHJ solar cell production is the limitation to comparably low post processing temperatures due to the deteriorating effect of high temperatures on the passivation properties of passivating intrinsic amorphous silicon (a‐Si:H(i)) layers combined with conventional boron‐doped amorphous silicon (a‐Si:H(p)) emitter layers.We present a boron‐doped nanocrystalline silicon oxide (nc‐SiOx:H(p)) material that exhibits superior annealing behaviour. Passivation stacks consisting of a‐Si:H(i) and nc‐SiOx:H(p) emitter layers show an increase in minority carrier lifetime with post deposition annealing temperatures ≤293 °C. To our knowledge, there have been no earlier reports showing that annealing of complete passivation stacks including p‐type silicon‐based layer at such high temperatures is beneficial.

Room-temperature fabrication of microwave dielectric Li2MoO4–TiO2 composite ceramics

This paper presents the incorporation of large amounts of insoluble additives into a Li2MoO4 ceramic matrix fabricated with a room-temperature densification method. Li2MoO4–TiO2 composite ceramics were fabricated by a method based on the water-solubility of Li2MoO4, where the densification occurs at room temperature during sample pressing and is followed by further post-processing at 120°C to remove residual water. As the amount of added rutile TiO2 rose from 10 to 30vol%, relative permittivity and loss tangent values at ~9GHz increased from 6.9 to 10.1 and from 1.1×10−3 to 3.8×10−3, respectively, and thermal coefficient of permittivity decreased from 180ppm/°C to −170ppm/°C. The Lichtenecker equation used in the analysis of relative permittivity was applicable in this study for Li2MoO4–TiO2 composite ceramics with up to 20vol% of TiO2. However, with 25 and 30vol% additions of TiO2, correspondence decreased due to increased porosity in the composites. The low post-processing temperature made this material feasible for Ag electrode integration without formation of unwanted extra phases.